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Dr. Sanza T. Kazadi

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Contact Information:

• Snail mail :
  Dr. Sanza Kazadi
  Jisan Research Institute
  515 S. Palm Ave., Suite 3
  Alhambra, CA 91803
  
• skazadi at jisan.org
  
• Phone : (626) 458-0000
  
• Fax : (626) 458-0001
  

Current Research Interests:

• Swarm Engineering

Former Research Interests:

• Evolutionary Systems
• Automatic Imitation Identification and Application

 

Publications:
Journal Papers:

• S. Kazadi, J. Wigglesworth, A. Grosz, A. Lim, and D. Vitullo. Swarm-Mediated Cluster-Based Construction . Complex Systems, 15(2), 157-181, 2004. (postscript) (PDF)
  Abstract
  We investigate the use of swarm clustering algorithms in the design of simple robots capable of carrying out swarm-mediated construction. Methods for generating multiple clusters of predetermined size are developed. Relative cluster motion algorithms are also developed and explored. All robotic algorithms are predicated on the use of only robots utilizing no processing, gps, or explicit communication. Simple stigmergic communication and minimal sensing capabilities are used exclusively. We demonstrate swarms of minimal agents building equilateral triangles, squares, and pentagons. Future use of these methods in the design of more sophisticated construction techniques is discussed.
  
  
• S. Kazadi, M. Chung, B. Lee, and R. Cho. On the Dynamics of Puck Clustering Systems . Robotics and Autonomous Systems , 46(1), pp. 1-27, 2004. (postscript) (PDF)
  Abstract
  We examine the theoretical foundations for the dynamics of puck clustering systems. Key in this investigation is the development of methods of controlling variance in cluster size, an important precursor to swarm-mediated clustering. We derive conditions under which clustering can take place in a general framework, and demonstrate two different behavioral regimes for clustering systems.
  
  
• S. Kazadi, A. Abdul-Khaliq, and R. Goodman. On the Convergence of Puck Clustering Systems . Robotics and Autonomous Systems , 38 (2), 93-117, 2002. (postscript) (PDF)
  
• S. Kazadi, R. Goodman, D. Tsikata, D. Green, H. Lin. An Autonomous Water Vapor Plume Tracking Robot Using Passive Resistive Polymer Sensors . Autonomous Robots , 9(2): 175-188, 2000. (postscript) (PDF)
  
• Conjugate Schema in the HP Heteropolymer Model of Protein Folding and Protein Design, S. Kazadi, H. Lin, P. Hung, D. Lee, D. Tsikata, J. Ogita, V. Huang, Complexity International volume 7 , 1999. (postscript) (PDF)
  
• Kazadi, S. Conjugate Schema and Basis Representation of Crossover and Mutation Evolutionary Computation , v6(2), 129-160, 1998. (postscript) (PDF)

Invited Book Chapter:

• S. Kazadi and J. Lee. Swarm Economicsi Advances in Computational Algorithms and Data Analysis Series: Lecture Notes in Electrical Engineering , Vol. 14 Ao, Sio-Iong; Rieger, Burghard; Chen, Su-Shing (Eds.) 2008,. (postscript)PDF
  

  Abstract

  
  The Hamiltonian Method of Swarm Design is applied to the design of an agent based economic system. The method allows the design of a system from the global behaviors to the agent behaviors, with a guarantee that once certain derived agent-level conditions are satisfied, the system behavior becomes the desired behavior. Conditions which must be satisfied by consumer agents in order to bring forth the ``invisible hand of the market" are derived and demonstrated in simulation. A discussion of how this method might be extended to other economic systems and non-economic systems is presented.

Invited Session Papers:
SCI2003 Conference
Special Session on Swarm Engineering

• S. Kazadi. The Genesis of Swarm Engineering. Proceedings of the SCI2003 Conference, Special Session on Swarm Engineering , Orlando, Florida, USA, 2003. (postscript) (PDF)
  Abstract
  This paper reviews the development of swarm engineering through various examples drawn from the literature. The paper explores the current swarm intelligence paradigm and describes some of the problems with this paradigm. Finally, the paper describes a new method called \textbf{\emph{swarm engineering}} which approaches the design and implementation of swarms in an entirely new way. The efficacy and limitations of this approach are discussed with examples from the literature.
  
  
• S. Kazadi. Eximius Distributed Processor. Proceedings of the SCI2003 Conference, Special Session on Swarm Engineering , Orlando, Florida, USA, 2003. (postscript) (PDF)
  Abstract
  This paper describes the Eximius distributed processor. The Eximius processor is a virtual processing platform which utilizes swarm engineering techniques in the maintenance of a computational swarm of processors. Under 700Kb in size, the processor has the capability to establish and restore communication with a virtually unlimited number of potential computational partners, as well as to draw new partners into the swarm at an exponentially increasing rate of information propagation. Any processor can utilize the power of other processors, with partitioning of processing time and addition and subtraction of new processors occurring seamlessly. We illustrate the capability of the processor on a small problem initiated from multiple machines of differing platforms and capabilities.
  
  
• S. Kazadi. Extension of Plume Tracking Behavior to Robot Swarms. Proceedings of the SCI2003 Conference, Special Session on Swarm Engineering , Orlando, Florida, USA, 2003. (postscript) (PDF)
  Abstract
  Generating an effective algorithm for plume tracking is a relatively straightforward task in plumes of large density. However, in the low density regime, plume tracking can be problematic, as a single packet odorant may not be sufficient to locate the source of the plume. We present work on the application of swarm engineering to the plume tracking problem. A swarm of mobile robots is designed which can track a virtual plume to it's source. We illustrate that the sensitivity of the swarm is significantly greater than that of the single robots, though the basic algorithm is identical on both sets of robots. We provide some motivation for the use of swarms of robots in specific regimes not well served by single robots.
  
  
• S. Kazadi. Position Control in Puck Clustering Systems. Proceedings of the SCI2003 Conference, Special Session on Swarm Engineering , Orlando, Florida, USA, 2003. (postscript) (PDF)
  Abstract
  In this paper, we examine position control of clusters emerging during the activity of puck clustering systems. Puck clustering systems are systems in which building material is continually picked up by simple agents moving in the system and deposited according to stochastic rules. We investigate the generation of multi- cluster systems in which clusters of predetermined size and number emerge as the clustering process continues. In this paper, we explore the performance of multi-cluster systems in which the positions of the clusters can be controlled.
  
  
• S. Kazadi. Mathematical Dynamics of Puck Clustering Systems. Proceedings of the SCI2003 Conference, Special Session on Swarm Engineering , Orlando, Florida, USA, 2003. (postscript) (PDF)
  Abstract
  Puck clustering involves the spatial rearrangement of building materials by manipulations of simple agents using local information. Thus far, this clustering has been described in the literature, but has little theoretical underpinning. Most imporant in the deficit in the literature is a solid theoretical description of clustering systems that can actually be built. This paper theoretically examines the long-term goal of clustering systems and provides methodology for the understanding of clustering dynamics as it pertains to the final clustering outcome and the variance in size of clusters of materials under the action of agents. We demonstrate that the dynamics of embodied systems differ from those of non-embodied systems, effectively creating two realms of clustering work.
  
  

Conference Papers:

• Sanza Kazadi, Paul Kim, John S. Lee, Joshua Lee Swarm Economics World Congress on Engineering and Computer Science 2007 , San Francisco, California, USA, October 24-26, 2007. (Postscript) (PDF)
  
  

  Abstract
  
  The Hamiltonian Method of Swarm Design is applied to the design of an agent based economic system. The method allows the design of a system from the global behaviors to the agent behaviors, with a guarantee that once certain derived agent-level conditions are satisfied, the system behavior becomes the desired behavior. Conditions which must be satisfied by consumer agents in order to bring forth the "invisible hand of the market" are derived and demonstrated in simulation. A discussion of how this method might be extended to other economic systems and non-economic systems is presented.
  
  

• Sanza Kazadi, John R. Lee, Julie Lee Artificial Physics, Swarm Engineering, and the Hamiltonian Method World Congress on Engineering and Computer Science 2007 , San Francisco, California, USA, October 24-26, 2007. (Postscript) (PDF)
  
  

  Abstract
  
  This paper describes the application of a swarm engineering methodology known as the Hamiltonian method of swarm design to the artificial physics problem. We demonstrate how to use this methodology to create swarms of predefined global properties by applying it to the basic artificial physics problem which creates locally hexagonal grids of agents, but fails to generate global hexagons. A condition for global hexagonal structure is derived, and two methods are described which accomplish this goal. Neither method requires global information.
  
  

• C. Lee, M. Kim, S. Kazadi Robot Clustering Proceedings of IEEE Conference on Systems, Man, and Cybernetics , Waikoloa, Hawaii, USA, pp.1449-1454, October 2005. (Postscript) (PDF)
  
  

  Abstract
  
  Puck clustering systems are systems in which simple agents move building material, or pucks, in a spatially limited area in a random or pseudo-random way. While we adapt puck clustering theory to robot clustering systems to generate a decentralized swarm of robots which coalesces using only stigmergic information and local sensing into a single cluster, this paper does not discuss puck clustering. Rather, its focus is on aggregation. Robot clustering systems may be characterized by the number of active robots in the system and the average variance of the robots from a determined center. The number of active robots decreases as cluster is formed, mirroring the analogous result of puck clustering. There is a sharp decline in the average variance of the robots, indicating a rapid coalescence of the robot swarm.
  
  

• K. Chang, J. Hwang, E. Lee, S. Kazadi The Application of Swarm Engineering Technique to Robust Multi-chain Robot System. Proceedings of IEEE Conference on Systems, Man, and Cybernetics , Waikoloa, Hawaii, USA, pp.1429-1434, October 2005. (Postscript) (PDF)
  
  

  Abstract
  
  The swarm engineering technique construed in {[}1{]} attempts to develop a general methodology that can be applied in creating swarm-mediated systems. In this companion paper, we utilize the system established in {[}2{]} to further explore the swarm engineering technique and show how this methodology can be used to develop a robust multi-chain robot system in a rigorous manner. In addition, we show the benefits of building a multi-chain robot system using the swarm engineering technique.
  
  

• S. Kazadi On the Development of a Swarm Engineering Methodology. Proceedings of IEEE Conference on Systems, Man, and Cybernetics , Waikoloa, Hawaii, USA, pp.1423-1428, October 2005. (Postscript) (PDF)
  
  

  Abstract
  
  This paper explores swarm engineering by revisiting popular concepts from are derived and demonstrated in simulation. A discussion of how this method might be extended to other economic systems and non-economic systems is presented.
  
  

• Sanza Kazadi, John R. Lee, Julie Lee Artificial Physics, Swarm Engineering, and the Hamiltonian Method World Congress on Engineering and Computer Science 2007 , San Francisco, California, USA, October 24-26, 2007. (Postscript) (PDF)
  
  

  Abstract
  
  This paper describes the application of a swarm engineering methodology known as the Hamiltonian method of swarm design to the artificial physics problem. We demonstrate how to use this methodology to create swarms of predefined global properties by applying it to the basic artificial physics problem which creates locally hexagonal grids of agents, but fails to generate global hexagons. A condition for global hexagonal structure is derived, and two methods are described which accomplish this goal. Neither method requires global information.
  
  

• C. Lee, M. Kim, S. Kazadi Robot Clustering Proceedings of IEEE Conference on Systems, Man, and Cybernetics , Waikoloa, Hawaii, USA, pp.1449-1454, October 2005. (Postscript) (PDF)
  
  

  Abstract
  
  Puck clustering systems are systems in which simple agents move building material, or pucks, in a spatially limited area in a random or pseudo-random way. While we adapt puck clustering theory to robot clustering systems to generate a decentralized swarm of robots which coalesces using only stigmergic information and local sensing into a single cluster, this paper does not discuss puck clustering. Rather, its focus is on aggregation. Robot clustering systems may be characterized by the number of active robots in the system and the average variance of the robots from a determined center. The number of active robots decreases as cluster is formed, mirroring the analogous result of puck clustering. There is a sharp decline in the average variance of the robots, indicating a rapid coalescence of the robot swarm.
  
  

• K. Chang, J. Hwang, E. Lee, S. Kazadi The Application of Swarm Engineering Technique to Robust Multi-chain Robot System. Proceedings of IEEE Conference on Systems, Man, and Cybernetics , Waikoloa, Hawaii, USA, pp.1429-1434, October 2005. (Postscript) (PDF)
  
  

  Abstract
  
  The swarm engineering technique construed in {[}1{]} attempts to develop a general methodology that can be applied in creating swarm-mediated systems. In this companion paper, we utilize the system established in {[}2{]} to further explore the swarm engineering technique and show how this methodology can be used to develop a robust multi-chain robot system in a rigorous manner. In addition, we show the benefits of building a multi-chain robot system using the swarm engineering technique.
  
  

• S. Kazadi On the Development of a Swarm Engineering Methodology. Proceedings of IEEE Conference on Systems, Man, and Cybernetics , Waikoloa, Hawaii, USA, pp.1423-1428, October 2005. (Postscript) (PDF)
  
  

  Abstract
  
  This paper explores swarm engineering by revisiting popular concepts from swarm intelligence and making them more rigorous by providing mathematical definitions. The definitions form the basis for an examination of an engineering methodology which starts by examining the desired state of a global property of the system and then generates a requirement for a local behavior that will generate the global property. This methodology allows a local behavior to be tested theoretically before it is tested empirically.
  
  

• S. Kazadi, M. Lee, L. Lee A Case for Exhaustive Optimization Proceedings of Gecco 2005 Conference, Late Breaking Papers , Washington D.C., USA, June 2005. (Postscript) (PDF)
  
  

  Abstract
  
  Evolutionary algorithms have enjoyed a great success in a variety of different fields ranging from numerical optimization to general creative design. However, to date, the question of why this success is possible has never been adequately determined. In this paper, we examine two algorithms, a genetic algorithm and a pseudo-exhaustive search algorithm dubbed Directed Exhaustive Search. We examine the GA's apparent ability to compound individual mutations, and its role in the GA's optimization. We then explore the use of the DES algorithm using a suitably altered mutation operator mimicking the GA's surreptitious compounding of the mutation operator. We find that the DES algorithm is capable of performing comparably to or outperforming the GA over all test problems, as predicted by theory.
  
  

• S. Kazadi, E. Kondo, A. Cheng. A Robost Centralized Linear Spatial Search Flock. Proceedings of IASTED International Conference Robotics and Applications, Honolulu, Hawaii, USA, pp.52-59, August 2004. (Postscript) (PDF)
  
  

  Abstract
  
  In this paper, we explore the development of a robust robot chain designed to allow non-pheromone-mediated target localization and transportation to a central location. The method is based on the development of a lossless robot swarm capable of carrying out a search of a local area in such a way that each individual robot is capable of determining the direction along the chain that one might follow to the center of search. We investigate some of the stability issues of the swarm, examining the stability during the setup phase, after the setup phase, and during catastrophic losses of individuals in the chain. We also explore potential methods of using the mechanism to deal with obstacles and multi-resource exploitation.
  
  

• S. Kazadi, O. Koroleva. Removing Degeneracy From Swarm Mediated Cluster-Based Construction. Proceedings of IASTED International Conference Robotics and Applications, Honolulu, Hawaii, USA, pp.60-66, August 2004. (Postscript) (PDF)
  
  

  Abstract
  
  
  
  In this paper we design swarm clustering algorithm to build, move and place clusters of building materials on the desired place on 2D plane. Such algorithm consists of three phases: building clusters, moving them radially and then orbitally. We present an example that demonstrates desired cluster placement. In particular, it is shown that with proposed algorithm capable of building clusters, moving and placing them with respect to each other in accordance with given requirements.
  
  
• S. Kazadi, D. Johnson, J. Melendez, B, Goo. Exhaustive Directed Search. Proceedings of the Genetic and Evolutionary Computation Conference, 2004 , Seattle, WA, USA, 2004. (postscript) (PDF)
  
  Abstract
  We explore the development of an exhaustive directed search of state space based on concepts from evolutionary computation. A brief investigation of the evolvability of an evolutionary algorithm illustrates that evolutionary algorithms are capable of reaching optimal solutions when the diversification operator (which may be a pseudo-operator which acts over many different diversification steps) is capable of reaching, at every improvement point, another, more improved population element. Moreover, we demonstrate that the upper limit on the time to the optimal point is identical to that of an exhaustive directed search. This search is exhaustive, but borrows the diversification operator from the evolutionary algorithm and proceeds in such a way that, if left alone, it would exhaustively search the space. However, we demonstrate that this type of search can perform comparably with the evolutionary algorithm, avoiding deceptive search tracks that might trap an evolutionary algorithm.
  
  
• S. Kazadi, D. Choi, A. Chang, T. Kang, H. Li, D. Kim, S. Ho, J. Wu. On the Design of an Evolutionary Preprocessor. Proceedings of the Genetic and Evolutionary Computation Conference, 2003 , Chicago, IL, USA, 2003. (postscript) (PDF)
  
  Abstract
  In this paper we explore methods of enhancing the evolvability of a particular device. We assume that the device may be specified by a table of inputs and outputs. We investigate a method of extracting the topologial structure of the device from rarified absolute Hessian matrices (raH matrices) and using this topological information as the basis for construction of solutions to evolutionary problems. We validate the algorithm by demonstrating its ability to extract the structure of devices to be evolved from the input/output table. Moreover, we validate this structure by using a genetic algorithm to train a perceptron, yielding perceptrons which solve the computational problem with error rates of less than 4%.
  
  
• S. Kazadi, S. Cheung, C. Ogletree, S. Kim, C. Lee, A. Min. A Study of Evolutionary Acceleration. Proceedings of the Genetic and Evolutionary Computation Conference, 2003 , Chicago, IL, USA, 2003. (postscript) (PDF)
  
  Abstract
  We investigate the phenomenon of numerical evolutionary acceleration. This phenomenon is a simple consequence of numerical analysis of the probabilities of evolving independent parts of a complex system in the presence of evolutionary epochs. The epoch mechanism allows the newly evolved structure to become part of the overall system design of all elements of the population. We demonstrated that this phenomenon not only exists in real evolving systems, but that evolutionary acceleration dwarfs the group mechanism for some complex structures.
  
  
• S. Kazadi, Y. Qi, I. Park, N. Huang, P. Hwu, B. Kwan, W. Lue, and H. Li. Insufficiency of Piecewise Evolution. Proceedings of the Third NASA/DoD Workshop on Evolvable Hardware , Long Beach, CA, 2001, pp. 223-231. (postscript) (PDF)
  
• S. Hoque, S. Kazadi, A. Li, W. Chen, and E. Sadun. Identification of Shapes Using a Nonlinear Dynamic System . Lecture Notes in Computer Science , 2095, pp.236-245, 2001. (postscript) (PDF)
  
• D. Lee, S. Kazadi, R. Goodman Swarm Engineering for TSP To appear in ANTS'2000 . (postscript) (PDF)
  
• S. Kazadi, D. Lee, R. Modi, J. Sy, and W. Lue. Levels of Compartamentalization in Artificial Life , Proceedings of Artificial Life VII , M. Bedau, S. McCaskill, N. Packard, and S. Rasmussen, eds., Cambridge, Massachusetts: MIT Press, 81-89, 2000. (postscript) (PDF)
  
• S. Kazadi, D. Lee, R. Modi, J. Sy, and W. Lue. Levels of Compartamentalization in Artificial Evolution , Proceedings of GECCO 2000 , 841-849, 2000. (postscript) (PDF)
  
• S. Rhee, J. Chung, S. Kazadi, H. Lin Conjugate Schema-Based Search JRI Technical Report 1 , 1999.
  
• Kazadi, S. Conjugate Schema in Genetic Search. Proceedings of the Seventh International Conference on Genetic Algorithms , San Mateo, Ca: Morgan Kaufmann Publishers, pp. 10-17, 1997. (postscript) (PDF)
  

PhD Thesis:


S. Kazadi Swarm Engineering Caltech PhD Thesis, 2000 . (postscript) (PDF)


Miscellaneous Papers:

• S. Kazadi. Swarm Engineering. IEEE Connections, p.7, 2004. (postscript) (PDF)
  
  Abstract
  
  This paper surveys the research on swarm engineering, a recent offshoot of swarm intelligence. In particular, we examine the motivation and approach of swarm engineers in developing swarm-based applications and propose a method thatis essentially top-down.
  

S. Kazadi, D. Lee, R. Modi, J. Sy, and W. Lue. Levels of Compartamentalization in Artificial Life , Proceedings of Artificial Life VII , M. Bedau, S. McCaskill, N. Packard, and S. Rasmussen, eds., Cambridge, Massachusetts: MIT Press, 81-89, 2000. (postscript) (PDF)

S. Kazadi, D. Lee, R. Modi, J. Sy, and W. Lue. Levels of Compartamentalization in Artificial Evolution , Proceedings of GECCO 2000 , 841-849, 2000. (postscript) (PDF)

S. Rhee, J. Chung, S. Kazadi, H. Lin Conjugate Schema-Based Search JRI Technical Report 1 , 1999.

Kazadi, S. Conjugate Schema in Genetic Search. Proceedings of the Seventh International Conference on Genetic Algorithms , San Mateo, Ca: Morgan Kaufmann Publishers, pp. 10-17, 1997. (postscript) (PDF)

PhD Thesis:


S. Kazadi Swarm Engineering Caltech PhD Thesis, 2000 . (postscript) (PDF)


Miscellaneous Papers:

• S. Kazadi. Swarm Engineering. IEEE Connections, p.7, 2004. (postscript) (PDF)
  
  Abstract
  
  This paper surveys the research on swarm engineering, a recent offshoot of swarm intelligence. In particular, we examine the motivation and approach of swarm engineers in developing swarm-based applications and propose a method thatis essentially top-down.